def test_dist(self, M1, M2):
s = signals.TDHQAMSymbols((M1, M2), 1000, fr=0.5)
d1_r = np.min(np.diff(np.unique(s._symbols_M1.real)))
d2_r = np.min(np.diff(np.unique(s._symbols_M2.real)))
d1_i = np.min(np.diff(np.unique(s._symbols_M1.imag)))
d2_i = np.min(np.diff(np.unique(s._symbols_M2.imag)))
npt.assert_approx_equal(d1_r, d2_r)
npt.assert_approx_equal(d1_i, d2_i)
def test_preserveattr(self, attr):
s1 = signals.TDHQAMSymbols((16, 4), 1000, 0.5)
s2 = s1 + 10
a1 = getattr(s1, attr)
a2 = getattr(s2, attr)
if isinstance(a1, np.ndarray):
npt.assert_array_almost_equal(a1, a2)
else:
assert a1 == a2
def test_from_arrays_ratio(self, r1, r2):
import math
if math.gcd(r1, r2) > 1:
assert True
return
r = r1 + r2
s1 = signals.SignalQAMGrayCoded(16, 1000 * r1, seed=[1, 2])
s2 = signals.SignalQAMGrayCoded(4, 1000 * r2, seed=[1, 2])
o = signals.TDHQAMSymbols.from_symbol_arrays(s1, s2, r2 / r)
o2 = signals.TDHQAMSymbols((16, 4), 1000 * (r1 + r2), fr=r2 / r, seed=[1, 2])
npt.assert_array_almost_equal(o, o2)
def test_ratio(self, r1, r2):
import math
if math.gcd(r1, r2) > 1:
assert True
return
r = r1 + r2
o = signals.TDHQAMSymbols((16, 4), 1000, fr=r2 / r)
for i in range(r):
s = o[0, i::r]
if i % r < r1:
d = np.min(abs(s[:, np.newaxis] - o._symbols_M1), axis=1)
npt.assert_array_almost_equal(d, 0)
else:
d = np.min(abs(s[:, np.newaxis] - o._symbols_M2), axis=1)
npt.assert_array_almost_equal(d, 0)
def testclass(self):
s = signals.TDHQAMSymbols((16, 4), 1000)
type(s._symbols_M1) is signals.SignalQAMGrayCoded
type(s._symbols_M2) is signals.SignalQAMGrayCoded
def test_attr_present(self, attr):
s = signals.TDHQAMSymbols((64, 128), 2**12)
assert getattr(s, attr) is not None
def test_tdhqam(self, dt):
s = signals.TDHQAMSymbols((64, 128), 2**12, dtype=dt)
assert np.dtype(dt) is s.dtype
assert np.dtype(dt) is s._symbols_M1.dtype
assert np.dtype(dt) is s._symbols_M2.dtype
